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- W2147780147 abstract "We introduce a copula-based dynamic model for multivariate processes of (non-negative) high-frequency trading variables revealing time-varying conditional variances and correlations. Modeling the variables' conditional mean processes using a multiplicative error model we map the resulting residuals into a Gaussian domain using a Gaussian copula. Based on high-frequency volatility, cumulative trading volumes, trade counts and market depth of various stocks traded at the NYSE, we show that the proposed copula-based transformation is supported by the data and allows capturing (multivariate) dynamics in higher order moments. The latter are modeled using a DCC-GARCH specification. We suggest estimating the model by composite maximum likelihood which is sufficiently flexible to be applicable in high dimensions. Strong empirical evidence for time-varying conditional (co-)variances in trading processes supports the usefulness of the approach. Taking these higher-order dynamics explicitly into account significantly improves the goodness-of-fit of the multiplicative error model and allows capturing time-varying liquidity risks." @default.
- W2147780147 created "2016-06-24" @default.
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- W2147780147 date "2012-07-12" @default.
- W2147780147 modified "2023-09-25" @default.
- W2147780147 title "Copula-Based Dynamic Conditional Correlation Multiplicative Error Processes" @default.
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- W2147780147 doi "https://doi.org/10.18452/4417" @default.
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